Carl Andersson: h-index, Total Citations, and Citation Map
Carl Andersson's h-index is 6 (5 i10-index, 2,096+ total citations across 5+ publications) according to Google Scholar as of May 2026. Carl Andersson is affiliated with Uppsala University.
Carl Andersson is a researcher affiliated with Uppsala University, specializing in various fields. Their work has been cited 2,096 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Carl Andersson's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 6
- i10-Index
- 5
- Total Citations
- 2,096
- Citing Countries
- 21
As of May 2026.
Carl Andersson has an h-index of 6 and 2,096 total citations across 5 publications, with research cited by institutions in 21 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Carl Andersson's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Top Cited Works
Tip: clickto hide a row from the map
Automatic diagnosis of the 12-lead ECG using a deep neural network
20201,338
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher advanced the application of deep convolutional networks to system identification, establishing a foundational framework that subsequent work expanded into broader deep learning methodologies for dynamic system modeling.
The researcher established a foundational framework for evaluating model calibration in classification, a seminal contribution that has become a standard reference point in the field.
The researcher developed a deep neural network for automatic 12-lead ECG diagnosis, a seminal contribution published in Nature Communications that has garnered over 1,300 citations.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.











